18,652 research outputs found
End-to-end representation learning for Correlation Filter based tracking
The Correlation Filter is an algorithm that trains a linear template to
discriminate between images and their translations. It is well suited to object
tracking because its formulation in the Fourier domain provides a fast
solution, enabling the detector to be re-trained once per frame. Previous works
that use the Correlation Filter, however, have adopted features that were
either manually designed or trained for a different task. This work is the
first to overcome this limitation by interpreting the Correlation Filter
learner, which has a closed-form solution, as a differentiable layer in a deep
neural network. This enables learning deep features that are tightly coupled to
the Correlation Filter. Experiments illustrate that our method has the
important practical benefit of allowing lightweight architectures to achieve
state-of-the-art performance at high framerates.Comment: To appear at CVPR 201
Generalized Kernel-based Visual Tracking
In this work we generalize the plain MS trackers and attempt to overcome
standard mean shift trackers' two limitations.
It is well known that modeling and maintaining a representation of a target
object is an important component of a successful visual tracker.
However, little work has been done on building a robust template model for
kernel-based MS tracking. In contrast to building a template from a single
frame, we train a robust object representation model from a large amount of
data. Tracking is viewed as a binary classification problem, and a
discriminative classification rule is learned to distinguish between the object
and background. We adopt a support vector machine (SVM) for training. The
tracker is then implemented by maximizing the classification score. An
iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page
Large Margin Object Tracking with Circulant Feature Maps
Structured output support vector machine (SVM) based tracking algorithms have
shown favorable performance recently. Nonetheless, the time-consuming candidate
sampling and complex optimization limit their real-time applications. In this
paper, we propose a novel large margin object tracking method which absorbs the
strong discriminative ability from structured output SVM and speeds up by the
correlation filter algorithm significantly. Secondly, a multimodal target
detection technique is proposed to improve the target localization precision
and prevent model drift introduced by similar objects or background noise.
Thirdly, we exploit the feedback from high-confidence tracking results to avoid
the model corruption problem. We implement two versions of the proposed tracker
with the representations from both conventional hand-crafted and deep
convolution neural networks (CNNs) based features to validate the strong
compatibility of the algorithm. The experimental results demonstrate that the
proposed tracker performs superiorly against several state-of-the-art
algorithms on the challenging benchmark sequences while runs at speed in excess
of 80 frames per second. The source code and experimental results will be made
publicly available
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